Machine Translation Weekly 58: Poisoning machine translation

Today, I am going to talk about a topic that is rather unknown to me: the safety and vulnerability of machine translation. I will comment on a paper Targeted Poisoning Attacks on Black-Box Neural Machine Translation by authors from the University of Melbourne and Facebook AI. The main issue making machine-translation users vulnerable is that they typically do not understand the target language and do not have any other choice than trusting the system that target-language output is adequate. Most […]

November 9, 2020 Classification, Machine Learning, NLP, Project, Python, Supervised, Text, Unstructured Data Leave a comment

Comprehensive Hands on Guide to Twitter Sentiment Analysis with dataset and code

Introduction Natural Language Processing (NLP) is a hotbed of research in data science these days and one of the most common applications of NLP is sentiment analysis. From opinion polls to creating entire marketing strategies, this domain has completely reshaped the way businesses work, which is why this is an area every data scientist must be familiar with. Thousands of text documents can be processed for sentiment (and other features including named entities, topics, themes, etc.) in seconds, compared to […]

November 9, 2020 Analytics Vidhya, Beginner, Career, Deep Learning, Listicle, Machine Learning, NLP, Resource Leave a comment

The 15 Most Popular Data Science and Machine Learning Articles on Analytics Vidhya in 2018

Introduction What is the one thing you enjoy most about Analytics Vidhya? The most popular answer we receive (and have received since Kunal transformed his idea into reality) is the content we publish. Our content is the one thing take pride in, and 2018 saw us take our high-quality content to a whole new level. We launched multiple top-quality and popular training courses, published knowledge-rich machine learning and deep learning articles and guides, and saw our blog visits cross 2.5 million […]

November 9, 2020 Deep Learning, Intermediate, Listicle, NLP, Python, Resource Leave a comment

8 Excellent Pretrained Models to get you Started with Natural Language Processing (NLP)

Introduction Natural Language Processing (NLP) applications have become ubiquitous these days. I seem to stumble across websites and applications regularly that are leveraging NLP in one form or another. In short, this is a wonderful time to be involved in the NLP domain. This rapid increase in NLP adoption has happened largely thanks to the concept of transfer learning enabled through pretrained models. Transfer learning, in the context of NLP, is essentially the ability to train a model on one dataset […]

November 9, 2020 Intermediate, NLP, Python, Technique, Text, Unstructured Data Leave a comment

How to Get Started with NLP – 6 Unique Methods to Perform Tokenization

Overview Looking to get started with Natural Language Processing (NLP)? Here’s the perfect first step Learn how to perform tokenization – a key aspect to preparing your data for building NLP models We present 6 different ways to perform tokenization on text data Introduction Are you fascinated by the amount of text data available on the internet? Are you looking for ways to work with this text data but aren’t sure where to begin? Machines, after all, recognize numbers, […]

November 9, 2020 Beginner, Data Mining, NLP, Python, Technique, Text, Unstructured Data Leave a comment

How Search Engines like Google Retrieve Results: Introduction to Information Extraction using Python and spaCy

Overview How do search engines like Google understand our queries and provide relevant results? Learn about the concept of information extraction We will apply information extraction in Python using the popular spaCy library – so a lot of hands-on learning is ahead! Introduction I rely heavily on search engines (especially Google) in my daily role as a data scientist. My search results span a variety of queries – Python code questions, machine learning algorithms, comparison of Natural Language Processing […]

November 9, 2020 Learning Path, NLP Leave a comment

A Comprehensive Learning Path to Understand and Master NLP in 2020

Introduction Google “NLP jobs” and a remarkable number of relevant searches show up. There are businesses spinning up around the world that cater exclusively to Natural Language Processing (NLP) roles! The industry demand for NLP experts has never been higher – and this is expected to increase exponentially in the next few years. But the supply side of things is falling short. Freshers and even experienced folks who want to land an NLP based role are struggling to break into […]

November 9, 2020 Intermediate, NLP, Project, Python, Text Leave a comment

Hands-on NLP Project: A Comprehensive Guide to Information Extraction using Python

Overview Information extraction is a powerful NLP concept that will enable you to parse through any piece of text Learn how to perform information extraction using NLP techniques in Python Introduction I’m a bibliophile – I love pouring through books in my free time and extracting as much knowledge as I can. But in today’s information overload age, the way we read stuff has changed. Most of us tend to skip the entire text, whether that’s an article, a […]

November 9, 2020 Advanced, Maths, NLP Leave a comment

Create a Pipeline to Perform Sentiment Analysis using NLP

This article was published as a part of the Data Science Blogathon. Overview Every basic fundamental and building block which is required for Sentiment Analysis. I’ve used an easy approach to explain all the basic concepts so that even a beginner reader would be able to get a thorough understanding of all the concepts. Topics: Preprocessing text, Vocabulary Corpus, Feature Extraction (Sparse Representation and Frequency Dictionary), Logistic Regression model for sentiment analysis. Sentiment Analysis is a supervised Machine Learning […]

November 9, 2020 Groovy

Recent Java enhancements for numeric calculations

In the past, slow evaluation of mathematical functions and large memory footprint were the most significant drawbacks of Java compared to C++/C for numeric computations and scientific data analysis. However, recent enhancements in the Java Virtual Machine (JVM) enabled faster and better numerical computing due to several enhancements in evaluating trigonometric functions. In this article we will use the DataMelt (https://datamelt.org) for our benchmarks. Let us consider the following algorithm implemented in the Groovy dynamically-typed language shown below. It uses […]

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